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  <h1>Source code for rl_coach.agents.value_optimization_agent</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
<span class="c1">#</span>
<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1"># you may not use this file except in compliance with the License.</span>
<span class="c1"># You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">OrderedDict</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">List</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">rl_coach.agents.agent</span> <span class="k">import</span> <span class="n">Agent</span>
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">ActionInfo</span><span class="p">,</span> <span class="n">StateType</span><span class="p">,</span> <span class="n">Batch</span>
<span class="kn">from</span> <span class="nn">rl_coach.filters.filter</span> <span class="k">import</span> <span class="n">NoInputFilter</span>
<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">screen</span>
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.prioritized_experience_replay</span> <span class="k">import</span> <span class="n">PrioritizedExperienceReplay</span>
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">DiscreteActionSpace</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span><span class="p">,</span> <span class="n">copy</span>


<span class="c1">## This is an abstract agent - there is no learn_from_batch method ##</span>


<span class="k">class</span> <span class="nc">ValueOptimizationAgent</span><span class="p">(</span><span class="n">Agent</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">agent_parameters</span><span class="p">,</span> <span class="n">parent</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s1">&#39;LevelManager&#39;</span><span class="p">,</span> <span class="s1">&#39;CompositeAgent&#39;</span><span class="p">]</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">agent_parameters</span><span class="p">,</span> <span class="n">parent</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">register_signal</span><span class="p">(</span><span class="s2">&quot;Q&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">q_value_for_action</span> <span class="o">=</span> <span class="p">{}</span>

        <span class="c1"># currently we use softmax action probabilities only in batch-rl,</span>
        <span class="c1"># but we might want to extend this later at some point.</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">should_get_softmax_probabilities</span> <span class="o">=</span> \
            <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">],</span> <span class="s1">&#39;should_get_softmax_probabilities&#39;</span><span class="p">)</span> <span class="ow">and</span>  \
            <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">should_get_softmax_probabilities</span>

    <span class="k">def</span> <span class="nf">init_environment_dependent_modules</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">init_environment_dependent_modules</span><span class="p">()</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spaces</span><span class="o">.</span><span class="n">action</span><span class="p">,</span> <span class="n">DiscreteActionSpace</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spaces</span><span class="o">.</span><span class="n">action</span><span class="o">.</span><span class="n">actions</span><span class="p">)):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">q_value_for_action</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">register_signal</span><span class="p">(</span><span class="s2">&quot;Q for action </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">),</span>
                                                                  <span class="n">dump_one_value_per_episode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                                                  <span class="n">dump_one_value_per_step</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="c1"># Algorithms for which q_values are calculated from predictions will override this function</span>
    <span class="k">def</span> <span class="nf">get_all_q_values_for_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="n">StateType</span><span class="p">):</span>
        <span class="n">actions_q_values</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">requires_action_values</span><span class="p">():</span>
            <span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">states</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">actions_q_values</span>

    <span class="k">def</span> <span class="nf">get_all_q_values_for_states_and_softmax_probabilities</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="n">StateType</span><span class="p">):</span>
        <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">requires_action_values</span><span class="p">():</span>
            <span class="n">outputs</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span>
            <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">output_heads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">softmax</span><span class="p">)</span>

            <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">outputs</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span>

    <span class="k">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prepare_batch_for_inference</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="s1">&#39;main&#39;</span><span class="p">),</span>
                                                            <span class="n">outputs</span><span class="o">=</span><span class="n">outputs</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">update_transition_priorities_and_get_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TD_errors</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
        <span class="c1"># update errors in prioritized replay buffer</span>
        <span class="n">importance_weights</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">PrioritizedExperienceReplay</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">&#39;update_priorities&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;idx&#39;</span><span class="p">),</span> <span class="n">TD_errors</span><span class="p">))</span>
            <span class="n">importance_weights</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">importance_weights</span>

    <span class="k">def</span> <span class="nf">_validate_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">policy</span><span class="p">,</span> <span class="n">action</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">action</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">():</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">((</span>
                <span class="s1">&#39;The exploration_policy </span><span class="si">{}</span><span class="s1"> returned a vector of actions &#39;</span>
                <span class="s1">&#39;instead of a single action. ValueOptimizationAgents &#39;</span>
                <span class="s1">&#39;require exploration policies which return a single action.&#39;</span>
            <span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">policy</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>

    <span class="k">def</span> <span class="nf">choose_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">curr_state</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">should_get_softmax_probabilities</span><span class="p">:</span>
            <span class="n">actions_q_values</span><span class="p">,</span> <span class="n">softmax_probabilities</span> <span class="o">=</span> \
                <span class="bp">self</span><span class="o">.</span><span class="n">get_all_q_values_for_states_and_softmax_probabilities</span><span class="p">(</span><span class="n">curr_state</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_all_q_values_for_states</span><span class="p">(</span><span class="n">curr_state</span><span class="p">)</span>

        <span class="c1"># choose action according to the exploration policy and the current phase (evaluating or training the agent)</span>
        <span class="n">action</span><span class="p">,</span> <span class="n">action_probabilities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">get_action</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">should_get_softmax_probabilities</span> <span class="ow">and</span> <span class="n">softmax_probabilities</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="c1"># override the exploration policy&#39;s generated probabilities when an action was taken</span>
            <span class="c1"># with the agent&#39;s actual policy</span>
            <span class="n">action_probabilities</span> <span class="o">=</span> <span class="n">softmax_probabilities</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">_validate_action</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="p">,</span> <span class="n">action</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">actions_q_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="c1"># this is for bootstrapped dqn</span>
            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span> <span class="o">==</span> <span class="nb">list</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">last_action_values</span>

            <span class="c1"># store the q values statistics for logging</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">q_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span>

            <span class="n">actions_q_values</span> <span class="o">=</span> <span class="n">actions_q_values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
            <span class="n">action_probabilities</span> <span class="o">=</span> <span class="n">action_probabilities</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>

            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">q_value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">):</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">q_value_for_action</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">q_value</span><span class="p">)</span>

            <span class="n">action_info</span> <span class="o">=</span> <span class="n">ActionInfo</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="n">action</span><span class="p">,</span>
                                     <span class="n">action_value</span><span class="o">=</span><span class="n">actions_q_values</span><span class="p">[</span><span class="n">action</span><span class="p">],</span>
                                     <span class="n">max_action_value</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">),</span>
                                     <span class="n">all_action_probabilities</span><span class="o">=</span><span class="n">action_probabilities</span><span class="p">)</span>

        <span class="k">else</span><span class="p">:</span>
            <span class="n">action_info</span> <span class="o">=</span> <span class="n">ActionInfo</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="n">action</span><span class="p">,</span> <span class="n">all_action_probabilities</span><span class="o">=</span><span class="n">action_probabilities</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">action_info</span>

    <span class="k">def</span> <span class="nf">learn_from_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;ValueOptimizationAgent is an abstract agent. Not to be used directly.&quot;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">run_off_policy_evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Run the off-policy evaluation estimators to get a prediction for the performance of the current policy based on</span>
<span class="sd">        an evaluation dataset, which was collected by another policy(ies).</span>
<span class="sd">        :return: None</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">ope_manager</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="p">,</span> <span class="n">NoInputFilter</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">reward_filters</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Defining a pre-network reward filter when OPEs are calculated will result in a mismatch &quot;</span>
                             <span class="s2">&quot;between q values (which are scaled), and actual rewards, which are not. It is advisable &quot;</span>
                             <span class="s2">&quot;to use an input_filter, if possible, instead, which will filter the transitions directly &quot;</span>
                             <span class="s2">&quot;in the replay buffer, affecting both the q_values and the rewards themselves. &quot;</span><span class="p">)</span>

        <span class="n">ips</span><span class="p">,</span> <span class="n">dm</span><span class="p">,</span> <span class="n">dr</span><span class="p">,</span> <span class="n">seq_dr</span><span class="p">,</span> <span class="n">wis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ope_manager</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
                                  <span class="n">evaluation_dataset_as_episodes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">evaluation_dataset_as_episodes</span><span class="p">,</span>
                                  <span class="n">evaluation_dataset_as_transitions</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">evaluation_dataset_as_transitions</span><span class="p">,</span>
                                  <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
                                  <span class="n">discount_factor</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">discount</span><span class="p">,</span>
                                  <span class="n">q_network</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="p">,</span>
                                  <span class="n">network_keys</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;main&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">input_embedders_parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>

        <span class="c1"># get the estimators out to the screen</span>
        <span class="n">log</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
        <span class="n">log</span><span class="p">[</span><span class="s1">&#39;Epoch&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training_epoch</span>
        <span class="n">log</span><span class="p">[</span><span class="s1">&#39;IPS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ips</span>
        <span class="n">log</span><span class="p">[</span><span class="s1">&#39;DM&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dm</span>
        <span class="n">log</span><span class="p">[</span><span class="s1">&#39;DR&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dr</span>
        <span class="n">log</span><span class="p">[</span><span class="s1">&#39;WIS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">wis</span>
        <span class="n">log</span><span class="p">[</span><span class="s1">&#39;Sequential-DR&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">seq_dr</span>
        <span class="n">screen</span><span class="o">.</span><span class="n">log_dict</span><span class="p">(</span><span class="n">log</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;Off-Policy Evaluation&#39;</span><span class="p">)</span>

        <span class="c1"># get the estimators out to dashboard</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">set_current_time</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_current_time</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">&#39;Inverse Propensity Score&#39;</span><span class="p">,</span> <span class="n">ips</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">&#39;Direct Method Reward&#39;</span><span class="p">,</span> <span class="n">dm</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">&#39;Doubly Robust&#39;</span><span class="p">,</span> <span class="n">dr</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">&#39;Sequential Doubly Robust&#39;</span><span class="p">,</span> <span class="n">seq_dr</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">&#39;Weighted Importance Sampling&#39;</span><span class="p">,</span> <span class="n">wis</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">get_reward_model_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Batch</span><span class="p">):</span>
        <span class="n">network_keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;reward_model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">input_embedders_parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
        <span class="n">current_rewards_prediction_for_all_actions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;reward_model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
            <span class="n">batch</span><span class="o">.</span><span class="n">states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">))</span>
        <span class="n">current_rewards_prediction_for_all_actions</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">batch</span><span class="o">.</span><span class="n">actions</span><span class="p">()]</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">rewards</span><span class="p">()</span>

        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">&#39;reward_model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">train_and_sync_networks</span><span class="p">(</span>
            <span class="n">batch</span><span class="o">.</span><span class="n">states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">),</span> <span class="n">current_rewards_prediction_for_all_actions</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>

    <span class="k">def</span> <span class="nf">improve_reward_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">epochs</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Train a reward model to be used by the doubly-robust estimator</span>

<span class="sd">        :param epochs: The total number of epochs to use for training a reward model</span>
<span class="sd">        :return: None</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">&#39;reward_model&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">batch_size</span>

        <span class="c1"># this is fitted from the training dataset</span>
        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="n">total_transitions_processed</span> <span class="o">=</span> <span class="mi">0</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">&#39;get_shuffled_training_data_generator&#39;</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)):</span>
                <span class="n">batch</span> <span class="o">=</span> <span class="n">Batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
                <span class="n">loss</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_reward_model_loss</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
                <span class="n">total_transitions_processed</span> <span class="o">+=</span> <span class="n">batch</span><span class="o">.</span><span class="n">size</span>

            <span class="n">log</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
            <span class="n">log</span><span class="p">[</span><span class="s1">&#39;Epoch&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">epoch</span>
            <span class="n">log</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="n">total_transitions_processed</span>
            <span class="n">screen</span><span class="o">.</span><span class="n">log_dict</span><span class="p">(</span><span class="n">log</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;Training Reward Model&#39;</span><span class="p">)</span>

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